Assistant Professor William Harcombe

CBS Ecology, Evolution & Behav
College of Biological Sciences
Twin Cities
Project Title: 
Microbial Ecosystem Prediction Using Multi-Species Metabolic Modeling

Natural microbial communities are composed of multiple species which are metabolically connected and whose interactions give rise to important emergent behaviors. For example, many microbes coexist within human guts (the microbiome), and the specific behavior of the microbiome can span from normal functioning to disease states depending on the species composition, the abiotic environment, and the interactions of the microbes with each other and their environment. Other examples of important metabolically connected microbial communities include multi-species biofilm ecosystems on medical devices or in industrial production, communities involved in the biodegredation of harmful chemicals, and soil microbial communities. Predicting how such communities function under different conditions is important yet extremely difficult. The Harcombe lab seeks to understand how a model three-species microbial community functions under different conditions and hopes to gain strong predictive ability with the use of metabolic modeling.

The group conducts many thousands of computer simulations that explore how the model community will perform, with different definitions of performance including biomass production and stability, in different abiotic environments and with different metabolic connectivities. Using their software platform COMETS (Computation of Microbial Ecosystems in Space and Time), they use the model species' metabolic networks to conduct dynamic flux balance analysis simulations over space and time, which predict how much each metabolic reaction should flux over each time step in order to optimize some objective, often biomass. Then, by manipulating the metabolic networks, for example simulating gene knockouts by forcing flux through a reaction to zero, they can learn how the connectivity within and among the species' metabolic networks causes different emergent behavior. So far, COMETS has been successfully used to predict growth profiles of the model community in a variety of circumstances, and has been used to predict how single gene knockouts affect the robustness of a two-species model community when the two species either compete for resources or are in an environment where they must cooperate to grow.

During 2019, the group plans do two main things using the resources at MSI. First, they will extend the single-gene knockout study to examine the effect of each metabolic reaction in our three-species community. Next, they will study the effects of knocking out two genes simultaneously, both within species and across species. After performing the simulations, they will test the predictions with high-throughput knockout experiments in the wet lab, and therefore gain an understanding of the strengths and limitations of COMETS.

These results will be of broad interest to researchers interested in the interactions of metabolism, ecology, and genetics. The researchers will obtain a nuanced view of how species metabolism interact with each other to affect a greater ecology, and will hopefully gain a predictive understanding that will give us tools that can be applied to applied ecosystems in future work. 

Project Investigators

Beth Adamowicz
Jeremy Anisman
Jeremy Chacon
Lisa Fazzino
Sarah Hammarlund
Assistant Professor William Harcombe
Brian Smith
Leno Smith Jr
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